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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

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Related Experiment Video

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Associative network applications to low-level machine vision.

J M Oyster, F Vicuna, W Broadwell

    Applied Optics
    |May 11, 2010
    PubMed
    Summary

    This study introduces associative networks, a parallel computing model, for faster low-level machine vision tasks. These networks enable rapid execution of image processing transformations previously too slow for sequential machines.

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Vision

    Background:

    • Low-level machine vision tasks are computationally intensive.
    • Sequential processing limits the practical application of many image transformations.
    • Parallel computational models offer potential for accelerating these tasks.

    Purpose of the Study:

    • To explore the application of associative networks to low-level machine vision.
    • To present a formal description of the associative network model.
    • To demonstrate the effectiveness of associative networks for image processing.

    Main Methods:

    • Formal description of the associative network model.
    • Design of associative networks for Boolean functions, edge detection, and Hough transform.

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  • Leveraging flexible processor interconnections for enhanced parallelism.

    • Main Results:

    • Associative networks were designed for key machine vision tasks.
    • The flexible interconnections facilitate novel parallel algorithm designs.
    • Demonstrated rapid execution of image processing transformations.

    Conclusions:

    • Associative networks are a viable and efficient parallel computational model for machine vision.
    • The model's flexibility surpasses other parallel approaches.
    • Accelerated image processing enhances the practicality of complex vision algorithms.